CN113051371A - Chinese machine reading understanding method and device, electronic equipment and storage medium - Google Patents
Chinese machine reading understanding method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
According to the Chinese machine reading understanding method, the Chinese machine reading understanding device, the electronic equipment and the storage medium, the problems and the texts are spliced to form the input text, and the sentences in the input text are subjected to word segmentation processing and word segmentation processing respectively to obtain the character sequence corresponding to the input text and the word segmentation sequence corresponding to the input text; performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first feature vector of a question and a text based on words; performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second feature vector of the question and the text based on words; performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector; inputting the fused feature vector into an LSTM network, and predicting the initial position and the end position of an answer in the text; by the method, semantic representation of the input text is enriched, and the accuracy of reading and understanding of the Chinese machine is improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of machine reading and understanding, in particular to a Chinese machine reading and understanding method and device, electronic equipment and a storage medium.
[ background of the invention ]
Machine-reading understanding is one of the most challenging tasks in the field of natural language processing, with the primary goal of improving the level of computer-reading understanding of text. In recent years, with the advent of pre-training language model technology, the reading understanding level of machines is also rapidly increasing, and particularly, the reading understanding level of English texts exceeds the human level, but the reading understanding level of Chinese texts is relatively low, so that a great space for improving the reading understanding level exists.
The SQuAD data set issued by Stanford university sets the standard answer of the question as a segment of text, and the machine reading understanding model aiming at the SQuAD data set belongs to the extraction type model. The extraction type machine reading understanding model extracts a segment from a text as an answer of a question based on the given text and the question proposed for the text.
In the prior art, most of the extraction type Chinese machine reading understanding algorithms are realized and improved based on an advanced pre-training model BERT, the accuracy rate is greatly improved compared with that of the traditional method, a certain difference exists between the accuracy rate and the human level, and a large promotion space exists.
[ summary of the invention ]
The invention aims to provide a reading and understanding method and device for a Chinese machine, electronic equipment and a storage medium, and aims to solve the technical problem that the reading and understanding accuracy of the Chinese machine is low in the prior art.
The technical scheme of the invention is as follows: the method for reading and understanding the Chinese language by the machine comprises the following steps:
splicing the question and the text to form an input text, and performing word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text;
performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first character vector of the question and the text based on words, wherein the improved BERT network is used for performing semantic extraction on the character sequence and comprises a transform encoder with residual connection;
performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text;
performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
and inputting the fused feature vector into an LSTM network, and predicting the initial position and the end position of the answer in the text.
Optionally, the performing word segmentation and word segmentation on the sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text includes:
carrying out stop word removal and special symbol removal processing on the input text to obtain a processed input text;
performing character-level coding on the processed input text by using a pre-training model BERT to obtain the character sequence;
and performing word level coding on the processed input text by using a deep learning word segmentation device to obtain the word segmentation sequence.
Optionally, the word vectorization processing is performed on the word segmentation sequence based on a preset external knowledge base to obtain a second feature vector of the question and the text based on words, and the word vectorization processing includes:
obtaining a semantic vector of each participle in the participle sequence from a preset external knowledge base to obtain a semantic expression sequence of the participle sequence;
obtaining a word vector of each participle in the participle sequence by using a word2vec model to obtain a word vector set of an input text;
acquiring and coding each semantic origin vector in the semantic representation sequence by using a word2vec model, and acquiring a semantic vector set of an input text based on the coded semantic origin vectors;
and splicing the word vector set and the semantic vector set to obtain the second feature vector.
Optionally, the performing feature fusion on the first feature vector and the second feature vector to obtain a fused feature vector includes:
splicing the first feature vector and the second feature vector to obtain a spliced feature vector;
inputting the spliced feature vector into a first full-connection layer, and performing fusion processing on the first feature vector and the second feature vector to obtain a high-dimensional fusion feature vector;
and inputting the high-dimensional fusion feature vector into a second full-connection layer, and performing dimensionality reduction on the high-dimensional fusion feature vector to obtain the fusion feature vector.
Optionally, after performing feature fusion on the first feature vector and the second feature vector to obtain a fused feature vector, the method further includes:
calculating the similarity between each word in the question and each word in the text according to the fusion feature vector to obtain a similarity matrix;
calculating a first attention weight matrix of the text to the question and a second attention weight matrix of the question to the text according to the similarity matrix, wherein the first attention weight matrix comprises the attention weight of each participle in the text to the question, and the second attention weight matrix comprises the attention weight of each participle in the question to the text;
and splicing the first attention weight matrix and the second attention weight matrix to form a final attention weight matrix, and performing attention weight processing on the fusion feature vector according to the final attention weight matrix to obtain the adjusted fusion feature vector.
Optionally, the training step of the LSTM network includes:
obtaining a sample input text, wherein the sample input text comprises a sample question, a sample text spliced with the sample question, and a real starting position and a real ending position of an answer marked in the sample text;
respectively performing word segmentation processing and word segmentation processing on sentences in the sample input text to obtain a character sequence corresponding to the sample input text and a word segmentation sequence corresponding to the input text;
performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first character vector of the question and the text based on words; performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text; performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
inputting the fusion feature vector into an LSTM network, and outputting a predicted initial position and a predicted position of an answer in the sample text;
and calculating errors between the predicted initial position and the predicted end position and the real initial position and the real end position of the answer, and adjusting parameters of the LSTM network according to the errors.
Optionally, the number of the transform encoders between two transform encoders forming a residual connection in the modified BERT network is greater than or equal to a preset threshold.
The other technical scheme of the invention is as follows: provided is a Chinese machine reading understanding device, comprising:
the preprocessing module is used for splicing the problems and the texts to form an input text, and performing word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text;
a first feature extraction module, configured to perform word vectorization processing on the character sequence by using an improved BERT network to obtain a first feature vector of the question and the text based on words, where the improved BERT network is used to perform semantic extraction on the character sequence, and the improved BERT network includes a transform encoder with residual connection;
the second feature extraction module is used for carrying out word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second feature vector of the question and the text based on words;
the feature fusion module is used for performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
and the prediction module is used for inputting the fusion feature vector into an LSTM network and predicting the initial position and the end position of the answer in the text.
The other technical scheme of the invention is as follows: an electronic device is provided that includes a processor, and a memory coupled to the processor, the memory storing program instructions executable by the processor; the processor, when executing the program instructions stored by the memory, implements the chinese machine reading understanding method described above.
The other technical scheme of the invention is as follows: there is provided a storage medium having stored therein program instructions that, when executed by a processor, implement the chinese machine reading understanding method described above.
The invention has the beneficial effects that: firstly, splicing questions and texts to form an input text, and respectively performing word segmentation processing and word segmentation processing on sentences in the input text to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text; then, carrying out word vectorization processing on the character sequence by utilizing an improved BERT network to obtain a first character vector of the question and the text based on words; then, carrying out word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second feature vector of the question and the text based on words; then, performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector; finally, inputting the fusion feature vector into an LSTM network, and predicting the initial position and the end position of an answer in the text; because the semantic extraction layer of the improved BERT network comprises a cross-layer connected transform coder, the extracted first feature vector has richer semantic information, which is beneficial to improving the reading and understanding accuracy of a Chinese machine, and meanwhile, the gradient disappearance phenomenon in the BERT network training process is effectively prevented, so that the BERT network can be better converged; the method has the advantages that the semantic representation of the input text is performed by introducing an external knowledge base to obtain a second feature vector, and the first feature vector and the second feature vector are subjected to feature fusion, so that the semantic representation of the input text is further enriched, and the accuracy of reading and understanding of a Chinese machine is improved.
[ description of the drawings ]
FIG. 1 is a flow chart of a reading understanding method of a Chinese machine according to a first embodiment of the present invention;
fig. 2 is a diagram showing an exemplary structure of a BERT network improved in the first embodiment of the present invention;
FIG. 3 is a flow chart of a reading understanding method of a Chinese machine according to a second embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a reading and understanding apparatus for Chinese machine according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the invention;
fig. 6 is a schematic structural diagram of a storage medium according to a fifth embodiment of the present invention.
[ detailed description ] embodiments
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first", "second" and "third" in the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. All directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the movement, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a flow chart of a method for reading and understanding a chinese machine according to a first embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method for reading and understanding the chinese language by machine includes the steps of:
s101, splicing the questions and the texts to form an input text, and performing word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text.
The application context of the embodiment is a pop-up reading comprehension, in which the answer to the question is a continuous segment in the text. The questions and the texts are both Chinese characters, the questions and the texts are directly spliced to form input texts, and the input texts are sentence pairs comprising the questions and the texts.
When the word segmentation is carried out, the input text is subjected to word deactivation and special symbol removal processing, and then a deep learning word segmentation device (Tokenizer) is used for word segmentation. Specifically, the input text with the stop words and the special symbols removed may be encoded at a character level by using a pre-training model BERT (Bidirectional encoded Representation from a transformer), so as to obtain an initial word vector Representation, that is, the input text corresponds to a character sequence. The stop words are words which are used in information retrieval and can automatically filter out certain characters or words before or after processing natural language texts in order to save storage space and improve search efficiency, and the stop words mainly comprise English characters, numbers, mathematical characters, punctuation marks, single Chinese characters with high use frequency and the like. The special characters are symbols which are less frequently used and difficult to directly input than conventional or commonly used symbols, such as mathematical symbols, unit symbols, tab symbols, and the like. The purpose of removing stop words and special symbols is to make the input text more concise and improve the efficiency of reading and understanding.
When the word segmentation is carried out, the input text is subjected to word-stop-removing and special symbol-removing processing, and then word segmentation is carried out by using a deep learning word segmentation device. Specifically, word segmentation tools such as jieba word segmentation, pkuseg word segmentation or nutac word segmentation can be used to encode the input text with the stop words and special symbols removed at word level to obtain an initial word vector representation, i.e. the input text corresponds to a word segmentation sequence.
Wherein the character sequence comprises a character sequence of the question (initial word vector of the question) and a character sequence of the text (initial word vector of the text), and the word segmentation sequence comprises a word segmentation sequence of the question (initial word vector of the question) and a word segmentation sequence of the text (initial word vector of the text).
And S102, carrying out word vectorization processing on the character sequence by utilizing an improved BERT network to obtain a first character vector of the question and the text based on words, wherein the improved BERT network is used for carrying out semantic extraction on the character sequence and comprises a transform coder with residual error connection.
In step S102, the improved BERT network includes an input layer, an output layer, and a plurality of transform encoders (transform encoders) disposed between the input layer and the output layer, where the input layer, the first transform Encoder, the second transform Encoder, … …, the nth transform Encoder, and the output layer are sequentially connected, and a residual connection is added between two transform encoders of different layers, where the number of residual connections is at least one, such as one, two, and three; further, a residual concatenation may be added between the upper and lower layers, i.e. two transform encoders forming different layers of the residual concatenation, one being the upper layer (e.g. 12 th transform encoder), one being the lower layer (e.g. 2 nd transform encoder), the number of transform encoders between two transform encoders forming a residual concatenation can be set to be greater than or equal to a preset threshold, for example, referring to fig. 2, in the improved BERT network structure shown in fig. 2, the 1 st layer and the 11 th layer are connected by the residual, and the 2 nd layer and the 12 th layer are connected by the residual, in fact, there are many combinations of the residual connection modes (for example, the 1 st layer and the 6 th layer are connected by the residual, and the 2 nd layer and the 8 th layer are connected by the residual (the number of residual connections is 2), the 1 st layer and the 3 rd layer are connected by the residual, and the 2 nd layer and the 4 th layer are connected by the residual, and the 6 th layer and the 8 th layer are connected by the residual (the number of residual connections is 3), etc.); the number of residual connections may be in various combinations, and is not limited to the combination of 2 in fig. 2.
The improved BERT network is used for performing semantic extraction on a character sequence and outputting word vectors fusing sentence semantics, the word vectors fusing the sentence semantics after the semantic extraction are first feature vectors, and the Transformer encoders are used for identifying the dependency and time sequence features of words in an input text; the attention mechanism is used for keeping each word vector in the input text containing information of all the word vectors in the input text; residual connection and layer standardization is used for performing residual connection (addition) on the output of the word vector and position coding and the output of the attention mechanism, and then performing normalization processing on the output after residual connection; the feedforward part consists of two layers of linear mapping and is activated by a ReLU activation function, and a hidden sequence of input text is output. The higher-layer Transformer encoder in the two different Transformer encoders connected in the residual error finally outputs the sum of the hidden sequence of the lower-layer Transformer encoder and the hidden sequence of the higher-layer Transformer encoder to the next layer of the higher-layer Transformer encoder.
In the embodiment, by integrating cross-layer residual error connection into the BERT network, the improved BERT network effectively avoids the gradient disappearance phenomenon in the model training process, can better converge, and is beneficial to improving the accuracy of Chinese reading understanding.
S103, carrying out word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain the second feature vector of the question and the text based on words.
In step S103, by introducing an external knowledge base, the semantic information of each participle is embedded in the participle sequence of the input text. The possible word senses are selected through context, the connection is established with the sememe of the word, the sememe and the word sense can be subjected to Embedding, the word similarity calculation and the analogy reasoning task are greatly improved, the word sense disambiguation can select the word sense according to the sememe, and the interpretability is improved.
In an optional embodiment, step S103 specifically includes:
and S1031, obtaining the semantic vector of each participle in the participle sequence from a preset external knowledge base, and obtaining the semantic expression sequence of the participle sequence.
S1032, obtaining the word vector of each word in the word segmentation sequence by using a word2vec model, and obtaining a word vector set of the input text.
S1033, obtaining and coding each semantic primitive vector in the semantic representation sequence by using a word2vec model, and obtaining a semantic vector set of the input text based on the coded semantic primitive vectors.
S1034, the word vector set and the semantic vector set are spliced to obtain the second feature vector.
In the present embodiment, the external knowledge base is HowNet, specifically, each word meaning information is labeled with an semantic, HowNet regards an semantic as an atomic semantic unit, and each part and attribute of the emphasized concept can be well expressed by the semantic.
In the present embodiment, a semantic representation sequence is formed by searching for a corresponding semantic in an external knowledge base for each participle in an input text. Further, each word segmentation in the input text may correspond to a plurality of sememes in an external knowledge base, and a sememe with the largest part-of-speech similarity may be selected as a corresponding sememe of the word segmentation by calculating part-of-speech similarities between the sememes and the corresponding word segmentation, specifically including the following steps: generating a part-of-speech vector corresponding to the participle according to the part-of-speech label of the participle in the participle sequence; respectively calculating the part-of-speech similarity of the part-of-speech vector of the participle and a plurality of semantic vectors; and selecting the original vector with the highest part-of-speech similarity as the original vector of the participle.
And S104, performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector.
In step S104, the semantic representation and the semantic representation of the input text are fused, and the semantic representation is adjusted according to the semantic representation, so that the semantic representation capability of the input text is enhanced.
Specifically, step S104 includes the steps of:
s1041, splicing the first feature vector and the second feature vector to obtain a spliced feature vector;
and summing the first eigenvector and the second eigenvector to realize splicing of the two eigenvectors.
S1042, inputting the splicing feature vector into a first full-connection layer, and performing fusion processing on the first feature vector and the second feature vector to obtain a high-dimensional fusion feature vector;
and S1043, inputting the high-dimensional fusion feature vector into a second full-connection layer, and performing dimensionality reduction on the high-dimensional fusion feature vector to obtain the fusion feature vector.
Wherein, first full articulamentum and second full articulamentum carry out two full articulamentums of equidimension not, first full articulamentum and second full articulamentum respectively.
And S105, inputting the fusion feature vector into an LSTM network, and predicting the initial position and the end position of the answer in the text.
In step S105, inputting the fused feature vector into a first classifier to obtain a first output result, and performing normalization processing on the first output result to obtain a first probability feature vector, where the first probability feature vector includes a probability that each word in the text is a starting position of an answer; inputting the fusion feature vector into a second classifier to obtain a second output result, and performing normalization processing on the second output result to obtain a second probability feature vector, wherein the second probability feature vector comprises the probability that each word segmentation in the text is the termination position of an answer; and taking the participle with the highest probability in the first probability feature vector as the starting position of the answer, and taking the participle with the highest probability in the second probability feature vector as the ending position of the answer.
In an optional embodiment, after step S104 and before step S105, the method further includes:
s201, calculating the similarity between each participle in the question and each participle in the text according to the fusion feature vector to obtain a similarity matrix;
wherein the fused feature vector comprises problem fused features spliced with each otherA vector (the feature of each word in the question) and a text fusion feature vector (the feature of each word in the text). Specifically, a similarity function is used for calculating word segmentation characteristics Q in the problemiAnd word segmentation feature T in textjSimilarity of (2)ij＝α(Qi，Tj)。
S202, calculating a first attention weight matrix of the text to the question and a second attention weight matrix of the question to the text according to the similarity matrix, wherein the first attention weight matrix comprises the attention weight of each participle in the text to the question, and the second attention weight matrix comprises the attention weight of each participle in the question to the text;
wherein, each participle T in the textjFor participles Q in questioniThe sum of the weights of attention of (1); each participle T in textjWeight of attention A (T) to question Qj) The calculation method is as follows: dividing words T in similarity matrixjThe column is taken as a corresponding column weight vector, and the column weight vector is normalized.
Wherein, each participle Q in the questioniFor word segmentation T in textjThe sum of the weights of attention of (1); each participle Q in the questioniWeight of attention A (Q) for text Ti) The calculation method is as follows: dividing words Q in similarity matrixiAnd taking the located row as a corresponding row weight vector, and normalizing the row weight vector.
S203, splicing the first attention weight matrix and the second attention weight matrix to form a final attention weight matrix, and performing attention weight processing on the fusion feature vector according to the final attention weight matrix to obtain the adjusted fusion feature vector;
wherein, each participle T in the textjWeight of attention A (T) to question Qj) Respectively with the word Q in questioniThe multiplied values of the feature vectors are summed to obtain the participle Q in the problemiThe adjusted feature vector of (a); each participle Q in the questioniWeight of attention A (Q) for text Ti) Respectively with the word segmentation T in the textjThe values multiplied by the feature vectors are summed to obtain the word segmentation T in the textjThe adjusted feature vector of (2).
In an optional embodiment, the training step of the LSTM network specifically includes:
s301, obtaining a sample input text, wherein the sample input text comprises a sample question, a sample text spliced with the sample question, and a real starting position and a real ending position of an answer marked in the sample text;
s302, performing word segmentation processing and word segmentation processing on sentences in the sample input text respectively to obtain a character sequence corresponding to the sample input text and a word segmentation sequence corresponding to the input text;
the way of performing the word segmentation and word segmentation processing on the sentences in the sample input text is similar to the way of processing the input text in step S101, which is specifically referred to above.
S303, carrying out word vectorization processing on the character sequence by utilizing an improved BERT network to obtain the question and a first character vector of the text based on words; performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text; performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
the processing procedure of the training phase in step S303 is similar to the actual prediction procedure in steps S102 to S104, which is specifically referred to above.
S304, inputting the fusion feature vector into an LSTM network, and outputting a predicted initial position and a predicted position of an answer in the sample text;
s305, calculating the error between the predicted initial position and the predicted end position and the real initial position and the real end position of the answer, and adjusting the parameters of the LSTM network according to the error.
Where the error is signaled to adjust a parameter in the LSTM network.
Fig. 3 is a flow chart of a method for reading and understanding a chinese machine according to a second embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 3 if the results are substantially the same. As shown in fig. 3, the method for reading and understanding the chinese language by machine includes the steps of:
s401, the questions and the texts are spliced to form an input text, and the sentences in the input text are subjected to word segmentation processing and word segmentation processing respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text.
And S402, carrying out word vectorization processing on the character sequence by utilizing an improved BERT network to obtain a first character vector of the question and the text based on words, wherein the improved BERT network is used for carrying out semantic extraction on the character sequence and comprises a transform encoder with residual error connection.
And S403, performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain the second feature vector of the question and the text based on words.
S404, performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector.
S405, inputting the fusion feature vector into an LSTM network, and predicting the initial position and the end position of the answer in the text.
Steps S401 to S405 refer to the first embodiment specifically, and are not described in detail herein.
S406, establishing a feature set of the input text according to the first feature vector, the second feature vector and the fused feature vector, and uploading the feature set to a block chain, so that the block chain encrypts and stores the feature set.
Specifically, the corresponding digest information is obtained based on the feature set of the input text, and specifically, the digest information is obtained by hashing the feature set of the input text, for example, by using the sha256s algorithm. Uploading summary information to the blockchain can ensure the safety and the fair transparency of the user. The user device may download the summary information from the blockchain to verify that the feature set of the input text has been tampered with. The blockchain referred to in this example is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Fig. 4 is a schematic structural diagram of a chinese machine reading understanding apparatus according to a third embodiment of the present invention. As shown in fig. 4, the chinese machine reading understanding apparatus 30 includes a preprocessing module 31, a first feature extraction module 32, a second feature extraction module 33, a feature fusion module 34, and a prediction module 35, where the preprocessing module 31 is configured to splice a question and a text to form an input text, and perform word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text; a first feature extraction module 32, configured to perform word vectorization processing on the character sequence by using an improved BERT network to obtain a first feature vector based on words of the question and the text, where the improved BERT network is used to perform semantic extraction on the character sequence, and the improved BERT network includes a transform encoder with residual connection; the second feature extraction module 33 is configured to perform word vectorization processing on the word segmentation sequence based on a preset external knowledge base, and obtain a second feature vector of the question and the text based on words; a feature fusion module 34, configured to perform feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector; and the prediction module 35 is configured to input the fused feature vector into an LSTM network, and predict an initial position and a final position of an answer in the text.
Further, the preprocessing module 31 is further configured to perform stop word removal and special symbol removal processing on the input text to obtain a processed input text; performing character-level coding on the processed input text by using a pre-training model BERT to obtain the character sequence; and performing word level coding on the processed input text by using a deep learning word segmentation device to obtain the word segmentation sequence.
Further, the second feature extraction module 33 is further configured to obtain a semantic vector of each participle in the participle sequence from a preset external knowledge base, so as to obtain a semantic representation sequence of the participle sequence; obtaining a word vector of each participle in the participle sequence by using a word2vec model to obtain a word vector set of an input text; acquiring and coding each semantic origin vector in the semantic representation sequence by using a word2vec model, and acquiring a semantic vector set of an input text based on the coded semantic origin vectors; and splicing the word vector set and the semantic vector set to obtain the second feature vector.
Further, the feature fusion module 34 is further configured to splice the first feature vector and the second feature vector to obtain a spliced feature vector; inputting the spliced feature vector into a first full-connection layer, and performing fusion processing on the first feature vector and the second feature vector to obtain a high-dimensional fusion feature vector; and inputting the high-dimensional fusion feature vector into a second full-connection layer, and performing dimensionality reduction on the high-dimensional fusion feature vector to obtain the fusion feature vector.
Further, the device 30 for reading and understanding by a chinese machine further includes an attention module, configured to calculate a similarity between each participle in the question and each participle in the text according to the fusion feature vector, so as to obtain a similarity matrix; calculating a first attention weight matrix of the text to the question and a second attention weight matrix of the question to the text according to the similarity matrix, wherein the first attention weight matrix comprises the attention weight of each participle in the text to the question, and the second attention weight matrix comprises the attention weight of each participle in the question to the text; and splicing the first attention weight matrix and the second attention weight matrix to form a final attention weight matrix, and performing attention weight processing on the fusion feature vector according to the final attention weight matrix to obtain the adjusted fusion feature vector.
Further, the chinese machine reading understanding apparatus 30 further includes a training module, configured to obtain a sample input text, where the sample input text includes a sample question, a sample text spliced with the sample question, and a real start position and a real end position of an answer labeled in the sample text; respectively performing word segmentation processing and word segmentation processing on sentences in the sample input text to obtain a character sequence corresponding to the sample input text and a word segmentation sequence corresponding to the input text; performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first character vector of the question and the text based on words; performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text; performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector; inputting the fusion feature vector into an LSTM network, and outputting a predicted initial position and a predicted position of an answer in the sample text; and calculating errors between the predicted initial position and the predicted end position and the real initial position and the real end position of the answer, and adjusting parameters of the LSTM network according to the errors.
Further, in the first feature extraction module 32, the number of the transform encoders between two transform encoders forming a residual connection in the modified BERT network is greater than or equal to a preset threshold.
Fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. As shown in fig. 5, the electronic device 40 includes a processor 41 and a memory 42 coupled to the processor 41.
The memory 42 stores program instructions for implementing the chinese machine reading understanding method of any of the above embodiments.
The processor 41 is operative to execute program instructions stored in the memory 42 for chinese machine reading understanding.
The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. The processor 41 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a storage medium according to a fifth embodiment of the invention. The storage medium of the embodiment of the present invention, which stores program instructions 51 that can implement all the methods described above, may be either non-volatile or volatile. The program instructions 51 may be stored in the storage medium in the form of a software product, and include several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
While the foregoing is directed to embodiments of the present invention, it will be understood by those skilled in the art that various changes may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A Chinese machine reading understanding method is characterized by comprising the following steps:
splicing the question and the text to form an input text, and performing word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text;
performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first character vector of the question and the text based on words, wherein the improved BERT network is used for performing semantic extraction on the character sequence and comprises a transform encoder with residual connection;
performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text;
performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
and inputting the fused feature vector into an LSTM network, and predicting the initial position and the end position of the answer in the text.
2. The method as claimed in claim 1, wherein the step of performing word segmentation and word segmentation on the sentences in the input text to obtain the character sequence corresponding to the input text and the word segmentation sequence corresponding to the input text comprises:
carrying out stop word removal and special symbol removal processing on the input text to obtain a processed input text;
performing character-level coding on the processed input text by using a pre-training model BERT to obtain the character sequence;
and performing word level coding on the processed input text by using a deep learning word segmentation device to obtain the word segmentation sequence.
3. The method as claimed in claim 1, wherein the performing word vectorization process on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text comprises:
obtaining a semantic vector of each participle in the participle sequence from a preset external knowledge base to obtain a semantic expression sequence of the participle sequence;
obtaining a word vector of each participle in the participle sequence by using a word2vec model to obtain a word vector set of an input text;
acquiring and coding each semantic origin vector in the semantic representation sequence by using a word2vec model, and acquiring a semantic vector set of an input text based on the coded semantic origin vectors;
and splicing the word vector set and the semantic vector set to obtain the second feature vector.
4. The method for Chinese machine reading comprehension according to claim 1, wherein the feature fusing the first feature vector and the second feature vector to obtain a fused feature vector comprises:
splicing the first feature vector and the second feature vector to obtain a spliced feature vector;
inputting the spliced feature vector into a first full-connection layer, and performing fusion processing on the first feature vector and the second feature vector to obtain a high-dimensional fusion feature vector;
and inputting the high-dimensional fusion feature vector into a second full-connection layer, and performing dimensionality reduction on the high-dimensional fusion feature vector to obtain the fusion feature vector.
5. The method for Chinese machine reading comprehension according to claim 1, wherein after the feature fusion of the first feature vector and the second feature vector to obtain a fused feature vector, the method further comprises:
calculating the similarity between each word in the question and each word in the text according to the fusion feature vector to obtain a similarity matrix;
calculating a first attention weight matrix of the text to the question and a second attention weight matrix of the question to the text according to the similarity matrix, wherein the first attention weight matrix comprises the attention weight of each participle in the text to the question, and the second attention weight matrix comprises the attention weight of each participle in the question to the text;
and splicing the first attention weight matrix and the second attention weight matrix to form a final attention weight matrix, and performing attention weight processing on the fusion feature vector according to the final attention weight matrix to obtain the adjusted fusion feature vector.
6. The method for Chinese machine reading comprehension of claim 1 wherein the step of training the LSTM network comprises:
obtaining a sample input text, wherein the sample input text comprises a sample question, a sample text spliced with the sample question, and a real starting position and a real ending position of an answer marked in the sample text;
respectively performing word segmentation processing and word segmentation processing on sentences in the sample input text to obtain a character sequence corresponding to the sample input text and a word segmentation sequence corresponding to the input text;
performing word vectorization processing on the character sequence by using an improved BERT network to obtain a first character vector of the question and the text based on words; performing word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second word-based feature vector of the question and the text; performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
inputting the fusion feature vector into an LSTM network, and outputting a predicted initial position and a predicted position of an answer in the sample text;
and calculating errors between the predicted initial position and the predicted end position and the real initial position and the real end position of the answer, and adjusting parameters of the LSTM network according to the errors.
7. The method of Chinese machine reading comprehension according to claim 1, wherein a number of said fransformer encoders between two said fransformer encoders forming a residual connection in said modified BERT network is greater than or equal to a preset threshold.
8. A Chinese machine reading understanding device is characterized by comprising:
the preprocessing module is used for splicing the problems and the texts to form an input text, and performing word segmentation processing and word segmentation processing on sentences in the input text respectively to obtain a character sequence corresponding to the input text and a word segmentation sequence corresponding to the input text;
a first feature extraction module, configured to perform word vectorization processing on the character sequence by using an improved BERT network to obtain a first feature vector of the question and the text based on words, where the improved BERT network is used to perform semantic extraction on the character sequence, and the improved BERT network includes a transform encoder with residual connection;
the second feature extraction module is used for carrying out word vectorization processing on the word segmentation sequence based on a preset external knowledge base to obtain a second feature vector of the question and the text based on words;
the feature fusion module is used for performing feature fusion on the first feature vector and the second feature vector to obtain a fusion feature vector;
and the prediction module is used for inputting the fusion feature vector into an LSTM network and predicting the initial position and the end position of the answer in the text.
9. An electronic device comprising a processor, and a memory coupled to the processor, the memory storing program instructions executable by the processor; the processor, when executing the program instructions stored in the memory, implements the method of Chinese machine reading according to any of claims 1-7.
10. A storage medium having stored therein program instructions, which when executed by a processor, implement the method of reading and understanding by a chinese machine according to any one of claims 1 to 7.
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